Final Report

[Pages:47]Final Report

Improving Public School Bus Operations Boston and Baltimore County Public Schools

Jianhe Du Virginia Tech Transportation Institute Phone: (540) 231-2673; Email: jdu@vtti.vt.edu

Youssef Bichiou Virginia Tech Transportation Institute Phone: (540) 231-1746; Email: ybichiou@vtti.vt.edu

Hesham Rakha Virginia Tech Transportation Institute Phone: (540) 231-1505; Email: hrakha@vt.edu

Young-Jae Lee Department of Transportation and Urban Infrastructure Studies,

Morgan State University Phone: (443) 443-885-1872; Email: YoungJae.Lee@morgan.edu

Amirreza Nickkar Department of Transportation and Urban Infrastructure Studies,

Morgan State University Email: amirreza.nickkar@morgan.edu

Date March 2020

Prepared for the Urban Mobility & Equity Center, Morgan State University, CBEIS 327, 1700 E. Coldspring Lane, Baltimore, MD 21251

ACKNOWLEDGMENT

This research was supported by the Urban Mobility & Equity Center at Morgan State University and the University Transportation Center(s) Program of the U.S. Department of Transportation.

Disclaimer

The contents of this report reflect the views of the authors, who are responsible for the facts and the accuracy of the information presented herein. This document is disseminated under the sponsorship of the U.S. Department of Transportation's University Transportation Centers Program, in the interest of information exchange. The U.S. Government assumes no liability for the contents or use thereof. ?Morgan State University, 2020. Non-exclusive rights are retained by the U.S. DOT.

1. Report No. UMEC-019

2. Government Accession No. 3. Recipient's Catalog No.

4. Title and Subtitle Improving Public School Bus Operations Boston and Baltimore County Public Schools

5. Report Date March, 2020

6. Performing Organization Code

7. Author(s) Include ORCID # Jianhe Du Youssef Bichiou Hesham A. Rakha Young-Jae Lee Amirreza Nickkar

8. Performing Organization Report No.

9. Performing Organization Name and Address Virginia Tech Transportation Institute 3500 Transportation Research Plaza Blacksburg, VA 24061

10. Work Unit No.

11. Contract or Grant No. 69A43551747123

12. Sponsoring Agency Name and Address US Department of Transportation Office of the Secretary-Research UTC Program, RDT-30 1200 New Jersey Ave., SE Washington, DC 20590

13. Type of Report and Period Covered Final December 2018 - March 2020

14. Sponsoring Agency Code

15. Supplementary Notes

16. Abstract Studies show that congestion in big cities has a tremendous impact on the time travelers spend on the road. This is translated into a loss of productivity and also impacts students relying on school buses to commute to their schools. In fact, a common problem facing schools is students arriving late for breakfast and/or classes. The objective of this research is to develop a system that allows the Boston Public Schools (BPS) and Baltimore County Public Schools (BCPS) to transport students to and from schools in a safe, reliable, and optimum manner. Due to BPS and BCPS's system of school choice and geography, some students need to travel long distances to attend school. This problem is complex and has many dimensions, and we built a system that uses historical and real-time traffic data to predict the traffic state evolution over a short time horizon. This is then coupled to an advanced routing algorithm to route buses in an optimal fashion to improve the quality of service.

17. Keywords: Transit/paratransit/ride-sharing, freight 18. Distribution Statement planning, urban mobility, optimum bus routing, cost efficiency, and equity.

19. Security Classif. (of this report): Unclassified

20. Security Classif. (of this page) 21. No. of Pages 22. Price

Unclassified

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Content

List of Figures....................................................................................................................................................iii List of Tables .....................................................................................................................................................iv Abstract ...............................................................................................................................................................v 1 Introduction ...............................................................................................................................................1 2 Literature Review.......................................................................................................................................2 3 Purpose of the Study.................................................................................................................................4 4 Accurate Estimation of the Travel Time on the Greater Boston Area Road Network..................5

4.1 Data Cleaning and Preparation .......................................................................................................6 4.2 Modeling Algorithm..........................................................................................................................7 4.3 Application of the Model...............................................................................................................11 5 Development of a Routing Algorithm for Buses................................................................................12 5.1 Proposed Algorithm .......................................................................................................................12 5.2 Examples ..........................................................................................................................................23 5.3 Results...............................................................................................................................................26 6 Summary and Conclusions.....................................................................................................................37 References .........................................................................................................................................................39

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List of Figures

Figure 1: Bus data points and Boston area.....................................................................................................7 Figure 2: Data processing flowchart ...............................................................................................................8 Figure 3: Travel speed: (a) a.m. peak; (b) zoomed in a.m. peak; (c) p.m. peak; (d) zoomed in p.m. peak ....................................................................................................................................................................10 Figure 4: The developed BRA for origin assignment .................................................................................19 Figure 5: The developed SA algorithm to solve the proposed SBRP ......................................................22 Figure 6: Simplified conceptual operation of school buses in a.m. trips .................................................24 Figure 7: Simplified conceptual operation of school buses in p.m. trips.................................................25 Figure 8: Locations of the schools in the hypothetical network...............................................................25 Figure 9: High school H1 student distribution and routings (a.m., DOC = 3)......................................26 Figure 10: High school H2 student distribution and routings (a.m., DOC = 3)....................................26 Figure 11: Middle school M1 student distribution and routings (a.m., DOC = 3)................................27 Figure 12: Middle school M2 student distribution and routings (a.m., DOC = 3)................................27 Figure 13: Middle school M3 student distribution and routings (a.m., DOC = 3)................................28 Figure 14: Elementary school (E1) student distribution and routings (a.m., DOC = 3) ......................28 Figure 15: Elementary school E2 student distribution and routings (a.m., DOC = 3).........................29 Figure 16: Elementary school E3 student distribution and routings (a.m., DOC = 3).........................29 Figure 17: Elementary school E4 student distribution and routings (a.m., DOC = 3).........................30 Figure 18: High school H1 student distribution and routings (p.m., DOC = 3) ...................................30 Figure 19: High school H2 student distribution and routings (p.m., DOC = 3) ...................................31 Figure 20: Middle school M1 student distribution and routings (p.m., DOC = 3) ...............................31 Figure 21: Middle school M2 student distribution and routings (p.m., DOC = 3) ...............................32 Figure 22: Middle school M3 student distribution and routings (p.m., DOC = 3) ...............................32 Figure 23: Elementary school E1 student distribution and routings (p.m., DOC = 3) ........................33 Figure 24: Elementary school E2 student distribution and routings (p.m., DOC = 3) ........................33 Figure 25: Elementary school E3 student distribution and routings (p.m., DOC = 3) ........................34 Figure 26: Elementary school E4 student distribution and routings (p.m., DOC = 3) ........................34

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List of Tables

Table 1: Times for Different Planning Periods .............................................................................................9 Table 2: Comparison of Models with DOC 3 and No DOC ...................................................................36 Table 3: Comparison of Total Costs and Indirect Trips with DOC 3 and No DOC ...........................37

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Abstract

Studies show that congestion in big cities has a tremendous impact on the time travelers spend on the road. This is translated into a loss of productivity and also impacts students relying on school buses to commute to their schools. In fact, a common problem facing schools is students arriving late for breakfast and/or classes. The objective of this research is to develop a system that allows the Boston Public Schools (BPS) and Baltimore County Public Schools (BCPS) to transport students to and from schools in a safe, reliable, and optimum manner. Due to BPS and BCPS's system of school choice and geography, some students need to travel long distances to attend school. This problem is complex and has many dimensions, and we built a system that uses historical and real-time traffic data to predict the traffic state evolution over a short time horizon. This is then coupled to an advanced routing algorithm to route buses in an optimal fashion to improve the quality of service.

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1 Introduction

Traffic congestion has become an everyday problem in many urban areas, bringing with it negative environmental impacts. During periods of congestion, cars and public transportation cannot run efficiently, resulting in air pollution, carbon dioxide (CO2) emissions, and increased fuel use. In 2007, wasted fuel and lost productivity cost Americans $87.2 billion. This number reached $115 billion in 2009 [1]. Congestion also increases travel time. For delivery companies and local bus transits systems, this is a source of delay, increased costs, and customer dissatisfaction.

Boston Public Schools (BPS) owns and operates a fleet of approximately 700 school buses that transport students throughout the Greater Boston area to their respective schools. On a typical day, an estimated 27,000 students are driven across the city to approximately 230 school locations [2]. For this logistical challenge to succeed, approximately 3,000 individual bus trips are needed. Since the students live "scattered" across the city, the buses, during rush hour, cover nearly 45,000 miles of almost all road types and, naturally, congestion levels. Equipped with a Global Positioning System (GPS) tracker, these buses are monitored in real time (i.e., position and velocity). BPS therefore has rich data that can be exploited to provide a live and accurate picture of the traffic through the city of Boston.

The logistical problem of delivering goods and/or people from starting locations to destinations and/or multiple destinations is not new. In fact, it is very similar to the bike share-rebalancing problem (BSRP) [3-5] and the problem of vehicle scheduling [6]. Given a number of bike stations scattered around a city, a truck or a group of trucks loops through to pick up excess bikes or drop off a number of needed bikes at each station. This operation is usually performed during the night, and thus does not face many constraints and is not time sensitive. However, in the bus routing problem, the operations need to be performed within a specific time frame since students need to arrive on time at schools to attend their classes. Given the ever-changing traffic patterns in big cities, the latter objective is challenging. The first solution schools adopt to improve on-time performance is to increase the number of buses, split the routes, and reassociate bus stops to the new routes. This is

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